One in five adults in the U.S. are thought to be living with a disability, a complex and multifaceted condition affecting physical movement patterns. Medical care costs related to disability in the US exceed $300 billion annually. Regular physical activity (PA) represents an important modifiable behavior to improve the overall health of these disabled groups and to realize their movement potential. Substantial progress has been made in developing and testing wearable monitor calibrations to predict physical activity (PA) level and type. However, little scientific attention has been given to develop methods to assess PA in disabled populations. The aims of this application focus on filling this scientific knowledge gap. Specific Aim 1) To measure impairment and function to provide a movement disorder analysis of: a) no movement limitation; b) upper extremity limitation; c) lower extremity limitation, or d) both upper and lower extremity limitation. Specific Aim 2) To measure bilateral wrist, hip, and ankle acceleration to conditionally model metabolic cost and activity type estimates. Our highly qualified research team will address the above aims by first carrying out a multitude of advanced upper and lower body strength, coordination, and functional motor tasks to isolate key impairment and function limitations across a diverse population with movement pattern differences. Then using an unsupervised learning and cluster analysis approach we will use these data to evaluate a fast, convenient, and simple metric to categorize functional movement pattern differences. We will assess criterion energy cost and multiple hip and limb acceleration during an extensive battery of activities of daily living, to guide the development of single or combined wearable motion sensor algorithms across categories of functional movement groups using advanced analytical techniques. The results of these proposed studies will for the first time provide an innovative and translatable approach to both categorize and assess PA in persons with movement limitations. This will present the foundation to evaluate a decision-tree approach to guide both monitor placement and algorithm choice to assess PA in diverse populations. Ultimately, our work will provide the potential to precisely and accurately assess PA prevalence rates, effectiveness of behavior-based PA interventions, and PA dose-response relationships to prevent and manage disabling conditions.